23 DOI: https://doi.org/10.1016/j.jgeb.2025.100569 English eng Pepe, Marco Contributor <p><em style="margin: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px;">Cannabis sativa</em><span style="color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px;"> L. (cannabis) has recently re-emerged as an economically important crop, fueling research focused on enhancing production practices to meet market demands. The developing cannabis industry can be improved by overcoming certain production hurdles using micropropagation to maintain and multiply pathogen-free plants in confined spaces at high volumes. However, developing efficient micropropagation systems for cannabis have been hampered by the prevalence of various morpho-physiological disorders, resulting in low multiplication rates, culture decline, and overall low efficiency rates. While progress in cannabis micropropagation has been made, nutrient imbalances and various disorders are still common. Successful micropropagation is species specific and dependent on a variety of interconnected factors related to abiotic conditions and nutrient availability, which represent challenges in the refinement and execution of effective methods. Micropropagation media represent the exclusive sources of macro- and micro-nutrients for cultured plant tissues, inadequacies of which can result in the emergence of morpho-physiological symptoms. This work represents the first in-depth analysis of multiple morpho-physiological disorders in micropropagated cannabis arising from media nutrient content. Additionally, we present machine learning as an effective tool for assessing nutrient-associated symptoms in cultured cannabis and identifying which components are responsible. Results will help with troubleshooting cannabis micropropagation systems to prevent or correct undesirable outcomes, while introducing new methods to assess </span><em style="margin: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px;">in vitro</em><span style="color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px;"> cannabis disorders.</span></p> Elsevier December 2025 pdf Journal of Genetic Engineering and Biotechnology Decoding the cannabis tissue culture puzzle: Machine learning analysis of cannabis in vitro morpho-physiological disorders expands the potential for precision micropropagation mixed material Elsevier